TY - GEN A1 - Fujarewicz, Krzysztof A1 - Wiench, Małgorzata A2 - Kimmel, Marek - red. A2 - Lachowicz, Mirosław - red. A2 - Świerniak, Andrzej - red. PB - Zielona Góra: Uniwersytet Zielonogórski N2 - DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. N2 - Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. N2 - The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumor/normal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. N2 - The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumor/normal colon data set. In this sense the RFR algorithm outperforms all other investigated methods. L1 - http://www.zbc.uz.zgora.pl/Content/59045/AMCS_2003_13_3_6.pdf L2 - http://www.zbc.uz.zgora.pl/Content/59045 KW - colon tumor KW - gene expression data KW - microarrays KW - support vector machines KW - feature selection KW - classification T1 - Selecting differentially expressed genes for colon tumor classification UR - http://www.zbc.uz.zgora.pl/dlibra/docmetadata?id=59045 ER -